Joint feature weighting and adaptive graph-based matrix regression for image supervised feature Selection

Matrix regression (MR) is a regression model that can directly perform on matrix data. However, the effect of each element in matrix data on regression model is different. Taking into consideration the relevance of every original feature in the matrix data and their influence on the final estimation of the regression model, we introduce an unknown weight matrix to encode the relevance of feature in matrix data and propose a feature weighting and graph-based matrix regression (FWGMR) model for image supervised feature selection. In this model, the feature weight matrix is used to select some important features from the matrix data and preserve the relative spatial location relationship of elements in the matrix data. In addition, in order to effectively and reasonably preserve the local manifold structure of the training matrix samples, a regularization term in the model is used to adaptively learn a graph matrix on low-dimensional space. An optimization algorithm is devised to solve FWGMR model and to provide the closed-form solutions of this model in each iteration. Extensive experiments on some public datasets demonstrate the superiority of FWGMR.     

SIFT与SURF特征提取算法在图像匹配中的应用对比研究

SIFT算法和SURF算法是图像特征提取和匹配的典型方法,广泛应用于目标检测、图像理解与识别等领域,然而对二者尚缺乏较深入的对比研究。针对这两种算法,采用实验室相机实拍图像和低空无人机实拍图像,以不同的图像旋转角度进行特征点提取和图像匹配实验,从匹配成功率和运行效率两个方面对算法的性能进行对比研究。结果表明,SIFT算法具有较好的图像旋转不变性,匹配精度较高,而SURF算法匹配精度较低,但是效率较高,因此在实际应用中可根据具体需求合理选择。     

红外图像目标特征提取方法

针对实际红外空中图像中目标的大小，方向等发生变化等情况，基于奇异值分解的特征向量稳定性不好。为解决这一技术难题，提出了基于尺度奇异值变换的红外图像目标特征提取方法。该方法是一种基于尺度变换的矩阵方法。同类目标图像样本经过上述变换后所得到的矩阵都具有相似的能量，用文中提出的矩阵表示方法对红外图像进行描述后，对其应的奇异值进行特征提取，克服了对原始图像的矩阵进行奇异值提时存在的缺点，实验表明该方法提取目标矩阵的奇异值特征向量具有较好的稳定性，对图像的平移，旋转和比例变化具有良好的不变性，是一种实用的特征提取方法。     

A robust classification procedure based on mixture classifiers and nonparametric weighted feature extraction

There are many factors to consider in carrying out a hyperspectral data classification. Perhaps chief among them are class training sample size, dimensionality, and distribution separability. The intent of this study is to design a classification procedure that is robust and maximally effective, but which provides the analyst with significant assists, thus simplifying the analyst's task. The result is a quadratic mixture classifier based on Mixed-LOOC2 regularized discriminant analysis and nonparametric weighted feature extraction. This procedure has the advantage of providing improved classification accuracy compared to typical previous methods but requires minimal need to consider the factors mentioned above. Experimental results demonstrating these properties are presented.     

Combinatorial discriminant analysis: supervised feature extraction that integrates global and local criteria

Supervised feature extraction (SFE) algorithms can be divided into two categories: those optimised by global criteria and those optimised by local ones. Proposed is a new approach designed by integrating global and local criteria, namely, combinatorial discriminant analysis (CDA), to perform SFE. It is shown that CDA inherits both the robustness of global criteria and the flexibility of local ones. Experimental comparisons with typical global and local SFE algorithms on real-world datasets empirically justify the superiority of CDA.     

指纹图像特征点提取算法研究

为了将指纹纹理中的有效信息提取出来,通过指纹图像预处理和特征点提取两个步骤,提出了一种指纹图像的特征点提取算法。图像预处理主要包含图像增强、二值化、细化,而特征点提取则采用模板搜索提取法。最终通过MATLAB GUI系统将采集的指纹图像进行处理,在用户界面内显示图像处理结果以及特征点提取结果,说明该设计很好地实现了算法目的,并且具有很好的稳定性和扩展性。     

图像多角度局部特征提取及相似性匹配技术研究

图像特征提取是计算机视觉应用的根本基础.研究了SIFT、LBP和HOG等3种信息互补的局部特征(即多角度局部特征)提取算法,研究了基于稀疏编码的图像相似性匹配算法,并以基于内容的图像检索(CBIR)为应用实例,验证了算法的有效性和高效性.     

New learning subspace method for image feature extraction

Image pattern recognition is the most i mportant re-search directions currently,and feature extraction is itscoretechnology.Researchinthisfield dates backat leastto the 1940s ,but most feature extraction techniques re-quire users to give a priori probabil…     

一种基于SAR图像的典型线特征抽取方法

针对合成孔径雷达(Synthetic Aperture Radar)图像的相干斑噪声会使图像的分割和目标的提取困难的问题,文中利用改进的Lee滤波手段去除相干斑噪声,并使用照度均衡算法平衡SAR图像的光照度,最后使用图割(Graph Cuts)算法交互式地对SAR图像中的线性结构进行抽取。实验结果表明Lee滤波和照度均衡算法分别对于抑制相干斑噪声干扰和修正图像对比度有显著效果,图割算法可以有效地提取出用户感兴趣的线性结构。     

Selection of image classifiers

An approach to classifier combination based on the concept of `dynamic classifier selection' is presented. The results reported show that the proposed approach enables effective image classification systems to be developed     

布里渊散射谱图像的边缘特征提取方法

为有效提取布里渊分布式光纤传感系统的布里渊频移,减少数据处理时间,提出一种基于二阶Laplacian边缘检测算子的布里渊散射光谱图像边缘特征提取方法.将布里渊频移视为散射谱图像边缘,利用二阶Laplacian边缘检测算子对布里渊散射光谱图像进行锐化处理;通过非极大值抑制和自适应阈值去除无效边缘获得二值图像,并搭建布里渊...     

基于OTSU和Canny算子的红外图像特征提取

机械设备运行过程中,标记的故障样本量小,导致建立的模型故障诊断准确率低,为此本文提出一种结合深度学习与域自适应的工件涡流热成像的缺陷检测方法。首先将注意力机制引入深度残差网络ResNet50中,加强模型的特征提取能力;然后将源域和目标域数据送入改进的ResNet50网络中提取深度特征,并且在网络的全连接层中引入局部最大均值差异,用于缩小两域特征间的分布差异,以此实现相关子域的分布对齐;最后在网络的Softmax分类器中实现对工件金属材料的缺陷检测。在公开的磁瓦数据集和本文实验采集的金属板涡流红外图像数据集上进行实验,结果表明,本文方法对涡流红外图像的裂纹缺陷检测识别准确率较高,通过t分布随机邻居嵌入方法对分析结果可视化,验证了本文方法的优越性。     

Gait recognition with shifted energy image and structural feature extraction

In this paper, we present a novel and efficient gait recognition system. The proposed system uses two novel gait representations, i.e., the shifted energy image and the gait structural profile, which have increased robustness to some classes of structural variations. Furthermore, we introduce a novel method for the simulation of walking conditions and the generation of artificial subjects that are used for the application of linear discriminant analysis. In the decision stage, the two representations are fused. Thorough experimental evaluation, conducted using one traditional and two new databases, demonstrates the advantages of the proposed system in comparison with current state-of-the-art systems.     

模糊算子在遥感图像特征提取中的应用

数据融合的概念应用在遥感图像的自动特征提取中,可以利用数据的互补和冗余来改善特征提取的性能。由于经典理论交集和并集的外延法不能得到满意的结果（相应的算子过于严格或是过于宽松）,对这两个具有对立特性的算子进行折衷,可以得到较为满意的结果。运用上下文依赖模糊算子实现这种折衷,对其加权因子的构造提出了新的设想和算法。参照计算机模拟的结果,分析和讨论了它的特点。     

A hybrid feature extraction method for SAR image registration

Extracting and matching correct correspondence between two images are significant stages for feature-based synthetic aperture radar (SAR) image registration. Two methods of feature extraction were employed in this study. Blob features were obtained by combining a Gaussian-guided filter (GGF) with a scale invariant feature transform, and corner features were obtained from the GGF. A GGF can store edge information and operate more effectively than a Gaussian filter. The ratio of average was used to compute gradients in order to reduce the speckle effect. Fast sample consensus (FSC) algorithm was combined with complete graph method for feature correspondence matching. Although FSC algorithm can extract valid correspondence, it may not be efficient enough to deal with SAR images due to its random nature and the large number of outliers in the data. Therefore, a graph-based algorithm was employed to solve the problem by eliminating outliers. The proposed hybrid method was tested on several real SAR images having different properties. The results showed that the proposed method performed the automated registration of SAR images more accurately and efficiently.     

An image segmentation based method for iris feature extraction

In this article, the local anomalistic blocks such as crypts, furrows, and so on in the iris are initially used directly as iris features. A novel image segmentation method based on intersecting cortical model （ICM） neural network was introduced to segment these anomalistic blocks. First, the normalized iris image was put into ICM neural network after enhancement. Second, the iris features were segmented out perfectly and were output in binary image type by the ICM neural network. Finally, the fourth output pulse image produced by ICM neural network was chosen as the iris code for the convenience of real time processing. To estimate the performance of the presented method, an iris recognition platform was produced and the Hamming Distance between two iris codes was computed to measure the dissimilarity between them. The experimental results in CASIA v1.0 and Bath iris image databases show that the proposed iris feature extraction algorithm has promising potential in iris recognition.     

光学相干层析图像的散斑去躁和边缘特征提取

提出采用各向异性扩散（AD）算法对光学相干层析（OCT）图像作噪声消除和边缘增强的预处理,然后结合改进的相位一致（PC）算法进行边缘特征提取,并引入巴特沃斯高通提升滤波进一步衰减低频噪声,增强高频边缘细节。对不同的OCT图像进行处理实验,结果表明,上述三种算法的综合应用,使OCT图像噪声得到有效抑制,轮廓清晰,特征部位得到加强。     

基于分块图像统计特征的红外目标提取

提出了一种基于分块图像统计特征的红外目标提取方法,首先将图像逐步分成越来越小的块,根据块图像统计特征构造函数求其极大值,获得目标的种子区域和包含整个目标的约束区域;然后在约束区域内,将一种快速的区域生长方法用于目标种子区域的生长,最终提取出红外目标。通过对不同目标大小的红外飞机图像的实验,验证了算法的有效性。对算法在TI公司的DSP芯片TMS320VC33 150上实现的时间消耗进行了估算,结果表明可以达到实时提取目标的要求。     

郊外低对比度图像线特征提取算法研究

景象匹配系统由于其精度高的特点,广泛应用于各种远程精确末制导系统。由于成像时的自然条件变化,使得基准图与实时图之间存在较大的对比度差别,增大了匹配难度。本文基于传统的特征提取,针对具有人工景物的郊外景象,利用Hough变换和数学形态学进行线特征的改进,从而提升了匹配质量。通过对实际情景图像的仿真分析,验证了该算法的有效性。     

Color image canonical correlation analysis for face feature extraction and recognition

Canonical correlation analysis (CCA) is a powerful statistical analysis technique, which can extract canonical correlated features from two data sets. However, it cannot be directly used for color images that are usually represented by three data sets, i.e., red, green and blue components. Current multi-set CCA (mCCA) methods, on the other hand, can only provide the iterative solutions, not the analytical solutions, when processing multiple data sets. In this paper, we develop the CCA technique and propose a color image CCA (CICCA) approach, which can extract canonical correlated features from three color components and provide the analytical solution. We show the mathematical model of CICCA, prove that CICCA can be cast as solving three eigen-equations, and present the realization algorithm of CICCA. Experimental results on the AR and FRGC-2 public color face image databases demonstrate that CICCA outperforms several representative color face recognition methods.